The causal effect of red blood cell folate on genome-wide methylation in cord blood: A Mendelian randomization approach

BMC Bioinformatics (Impact Factor: 2.58). 12/2013; 14(1):353. DOI: 10.1186/1471-2105-14-353
Source: PubMed


Investigation of the biological mechanism by which folate acts to affect fetal development can inform appraisal of expected benefits and risk management. This research is ethically imperative given the ubiquity of folic acid fortified products in the US. Considering that folate is an essential component in the one-carbon metabolism pathway that provides methyl groups for DNA methylation, epigenetic modifications provide a putative molecular mechanism mediating the effect of folic acid supplementation on neonatal and pediatric outcomes.
In this study we use a Mendelian Randomization (Mendelian Randomization) approach to assess the effect of red blood cell (RBC) folate on genome-wide DNA methylation in cord blood. Site-specific CpG methylation within the proximal promoter regions of approximately 14,500 genes was analyzed using the Illumina Infinium Human Methylation27 Bead Chip for 50 infants from the Epigenetic Birth Cohort at Brigham and Women's Hospital in Boston. Using methylenetetrahydrofolate reductase genotype as the instrument, the Mendelian Randomization approach identified 7 CpG loci with a significant (mostly positive) association between RBC folate and methylation level. Among the genes in closest proximity to this significant subset of CpG loci, several enriched biologic processes were involved in nucleic acid transport and metabolic processing. Compared to the standard ordinary least squares regression method, our estimates were demonstrated to be more robust to unmeasured confounding.
To the authors' knowledge, this is the largest genome-wide analysis of the effects of folate on methylation pattern, and the first to employ Mendelian Randomization to assess the effects of an exposure on epigenetic modifications. These results can help guide future analyses of the causal effects of periconceptional folate levels on candidate pathways.

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    ABSTRACT: Folate intake during pregnancy may affect the regulation of DNA methylation during fetal development. The genomic regions in the offspring that may be sensitive to folate exposure during in utero development have not been characterized. Using genome-scale profiling, we investigated DNA methylation in 2 immune cell types (CD4(+) and antigen-presenting cells) isolated from neonatal cord blood, selected on the basis of in utero folate exposure. High-folate (HF; n=11) and low-folate (LF; n=12) groups were selected from opposite extremes of maternal serum folate levels measured in the last trimester of pregnancy. A comparison of these groups revealed differential methylation at 7 regions across the genome. By far, the biggest effect observed was hypomethylation of a 923 bp region 3 kb upstream of the ZFP57 transcript, a regulator of DNA methylation during development, observed in both cell types. Levels of H3/H4 acetylation at ZFP57 promoter and ZFP57 mRNA expression were higher in CD4(+) cells in the HF group relative to the LF group. Hypomethylation at this region was replicated in an independent sample set. These data suggest that exposure to folate has effects on the regulation of DNA methylation during fetal development, and this may be important for health and disease.-Amarasekera, M., Martino, D., Ashley, S., Harb, H., Kesper, D., Strickland, D., Saffery, R., Prescott, S. L. Genome-wide DNA methylation profiling identifies a folate-sensitive region of differential methylation upstream of ZFP57-imprinting regulator in humans.
    The FASEB Journal 06/2014; 28(9). DOI:10.1096/fj.13-249029 · 5.04 Impact Factor
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    Future Neurology 07/2014; 9(4):397-400. DOI:10.2217/fnl.14.39
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    ABSTRACT: Background With the help of epigenome-wide association studies (EWAS), increasing knowledge on the role of epigenetic mechanisms such as DNA methylation in disease processes is obtained. In addition, EWAS aid the understanding of behavioral and environmental effects on DNA methylation. In terms of statistical analysis, specific challenges arise from the characteristics of methylation data. First, methylation β-values represent proportions with skewed and heteroscedastic distributions. Thus, traditional modeling strategies assuming a normally distributed response might not be appropriate. Second, recent evidence suggests that not only mean differences but also variability in site-specific DNA methylation associates with diseases, including cancer. The purpose of this study was to compare different modeling strategies for methylation data in terms of model performance and performance of downstream hypothesis tests. Specifically, we used the generalized additive models for location, scale and shape (GAMLSS) framework to compare beta regression with Gaussian regression on raw, binary logit and arcsine square root transformed methylation data, with and without modeling a covariate effect on the scale parameter. Results Using simulated and real data from a large population-based study and an independent sample of cancer patients and healthy controls, we show that beta regression does not outperform competing strategies in terms of model performance. In addition, Gaussian models for location and scale showed an improved performance as compared to models for location only. The best performance was observed for the Gaussian model on binary logit transformed β-values, referred to as M-values. Our results further suggest that models for location and scale are specifically sensitive towards violations of the distribution assumption and towards outliers in the methylation data. Therefore, a resampling procedure is proposed as a mode of inference and shown to diminish type I error rate in practically relevant settings. We apply the proposed method in an EWAS of BMI and age and reveal strong associations of age with methylation variability that are validated in an independent sample. Conclusions Models for location and scale are promising tools for EWAS that may help to understand the influence of environmental factors and disease-related phenotypes on methylation variability and its role during disease development.
    BMC Bioinformatics 07/2014; 15(1):232. DOI:10.1186/1471-2105-15-232 · 2.58 Impact Factor
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